[Colloquium] REMINDER: 3/7 Talks at TTIC: Wei-Lun (Harry) Chao, University of Southern California

[Mary Marre via Colloquium]

*When:     Wednesday, March 7th at 10:30 amWhere:    TTIC, 6045 S Kenwood
Avenue, 5th Floor, Room 526Who:       Wei-Lun (Harry) Chao, University of
Southern California*


*Title:*       Transfer Learning towards Intelligent Systems in the Wild

*Abstract:* Developing intelligent systems for vision and language
understanding in the wild has long been a crucial part that people dream
about the future. In the past few years, with the accessibility to
large-scale data and the advance of machine learning algorithms, vision and
language understanding has had significant progress for constrained
environments. However, it remains challenging for unconstrained
environments in the wild where the intelligent system needs to tackle
unseen objects and unfamiliar language usage that it has not been trained
on. Transfer learning, which aims to transfer and adapt the learned
knowledge from the training environment to a different but related test
environment has thus emerged as a promising paradigm to remedy the
difficulty.

In this talk, I will present my recent work on transfer learning towards
intelligent systems in the wild. I will begin with zero-shot learning,
which aims to expand the learned knowledge from seen objects, of which we
have training data, to unseen objects, of which we have no training data. I
will present an algorithm SynC that can construct classifiers of any object
class given its semantic description, even without training data, followed
by a comprehensive study on how to apply it to different environments. I
will then describe an adaptive visual question answering framework that
builds upon the insight of zero-shot learning and can further adapt its
knowledge to the new environment given limited information. I will finish
my talk with directions for future research.

*Bio: *Wei-Lun (Harry) Chao is a Computer Science PhD candidate at
University of Southern California, working with Fei Sha. His research
interests are in machine learning and its applications to computer vision
and artificial intelligence. His recent work has focused on transfer
learning towards vision and language understanding in the wild. His earlier
research includes work on probabilistic inference, structured prediction
for video summarization, and face understanding.


Host: Greg Shakhnarovich <greg at ttic.edu>




Mary C. Marre
Administrative Assistant
*Toyota Technological Institute*
*6045 S. Kenwood Avenue*
*Room 504*
*Chicago, IL  60637*
*p:(773) 834-1757*
*f: (773) 357-6970*
*mmarre at ttic.edu <mmarre at ttic.edu>*

On Wed, Feb 28, 2018 at 5:14 PM, Mary Marre <mmarre at ttic.edu> wrote:

>
>
> * When:     Wednesday, March 7th at 10:30 amWhere:    TTIC, 6045 S Kenwood
> Avenue, 5th Floor, Room 526Who:       Wei-Lun (Harry) Chao, University of
> Southern California*
>
>
> *Title:*       Transfer Learning towards Intelligent Systems in the Wild
>
> *Abstract:* Developing intelligent systems for vision and language
> understanding in the wild has long been a crucial part that people dream
> about the future. In the past few years, with the accessibility to
> large-scale data and the advance of machine learning algorithms, vision and
> language understanding has had significant progress for constrained
> environments. However, it remains challenging for unconstrained
> environments in the wild where the intelligent system needs to tackle
> unseen objects and unfamiliar language usage that it has not been trained
> on. Transfer learning, which aims to transfer and adapt the learned
> knowledge from the training environment to a different but related test
> environment has thus emerged as a promising paradigm to remedy the
> difficulty.
>
> In this talk, I will present my recent work on transfer learning towards
> intelligent systems in the wild. I will begin with zero-shot learning,
> which aims to expand the learned knowledge from seen objects, of which we
> have training data, to unseen objects, of which we have no training data. I
> will present an algorithm SynC that can construct classifiers of any object
> class given its semantic description, even without training data, followed
> by a comprehensive study on how to apply it to different environments. I
> will then describe an adaptive visual question answering framework that
> builds upon the insight of zero-shot learning and can further adapt its
> knowledge to the new environment given limited information. I will finish
> my talk with directions for future research.
>
> *Bio: *Wei-Lun (Harry) Chao is a Computer Science PhD candidate at
> University of Southern California, working with Fei Sha. His research
> interests are in machine learning and its applications to computer vision
> and artificial intelligence. His recent work has focused on transfer
> learning towards vision and language understanding in the wild. His earlier
> research includes work on probabilistic inference, structured prediction
> for video summarization, and face understanding.
>
>
> Host: Greg Shakhnarovich <greg at ttic.edu>
>
>
>
>
>
>
> Mary C. Marre
> Administrative Assistant
> *Toyota Technological Institute*
> *6045 S. Kenwood Avenue*
> *Room 504*
> *Chicago, IL  60637*
> *p:(773) 834-1757 <(773)%20834-1757>*
> *f: (773) 357-6970 <(773)%20357-6970>*
> *mmarre at ttic.edu <mmarre at ttic.edu>*
>
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